Cybersecurity threats are becoming increasingly sophisticated, frequent, and diverse, posing a major risk to critical infrastructure, public trust, and digital economies. Traditional intrusion detection systems often struggle with detecting novel or rare attack types, particularly when data availability is limited or heterogeneous. The current study tries to address these challenges by proposing a unified, multimodal threat detection framework that leverages the combination of synthetic data generation through Generative Adversarial Networks (GANs), advanced ensemble learning, and transfer learning techniques. The research objective is to enhance detection accuracy and resilience against zero-day, botnet, and image-based malware attacks by integrating multiple data modalities, including structured network logs and malware binaries, within a scalable and flexible pipeline. The proposed system features a dual-branch architecture: one branch uses a CNN with transfer learning for image-based malware classification, and the other employs a soft-voting ensemble classifier for tabular intrusion detection, both trained on augmented datasets generated by GANs. Experimental results demonstrate significant improvements in detection performance and false positive reduction, especially when multimodal outputs are fused using the proposed confidence-weighted strategy. The findings highlight the framework’s adaptability and practical applicability in real-world intrusion detection and response systems.
Πεππές et al. (Thu,) studied this question.